MixN_ChronicOrganoids

Data loading

suppressPackageStartupMessages({
  library(SummarizedExperiment)
  library(SEtools)
  library(edgeR)
  library(DT)
  library(pheatmap)
  library(plotly)
  library(dplyr)
  library(sva)
  library(reshape2)
  library(overlapper)
  library(ggrepel)
})
source("Functions/EDC_Functions.R")
source("Functions/CriFormatted_OLD.R")
source("Functions/CriFormatted.R")
load("Data/TotalSumExp.RData")
load("Data/geneLengths.RData")
load("Data/AllSEcorrected.RData",verbose=T)

## Loading objects:
##   SEs
##   DEAs

load("Data/brainspan.cortex.byAge.RData", verbose = T)

## Loading objects:
##   byAge

load("Data/iPSC.RData", verbose = T)

## Loading objects:
##   e

Brainspan correlation

Organoids <- assays(SEs$chronic.org[,which(SEs$chronic.org$EXPO=="CNT")])$counts
Fetal2D <- assays(SEs$chronic.fetal[,which(SEs$chronic.fetal$EXPO=="CNT")])$counts
iPSC <- e[,grep("3391B",colnames(e))]
Fetal <- byAge
i <- intersect(rownames(Organoids),intersect(rownames(iPSC),rownames(Fetal2D)))
tot <- cbind(Organoids[i,],iPSC[i,],Fetal2D[i,])

tot <- filterGenes(tot)
en <- donorm(tot)
#put the rows in the same order
i <- intersect(row.names(Fetal),row.names(en))
en <- en[i,]
Fetal <- Fetal[i,]
#create the matrix of pearson correlation coefficients and plot
m <- matrix(0,nrow=ncol(Fetal),ncol=ncol(en))
for(i in 1:ncol(en)){ m[,i] <- apply(Fetal,2,FUN=function(x){ cor(log(en[,i]+1),log(x+1)) }) }
row.names(m) <- colnames(Fetal)
colnames(m) <- colnames(en)
m2 <- m[grep("pcw",row.names(m)),]
byheatmap(m2,cluster_row=F)

DEGs heatmaps

org.chronic <- row.names(DEAs$chronic.org)[which((abs(DEAs$chronic.org$logFC.EXPO1X)>0.5 | abs(DEAs$chronic.org$logFC.EXPO1000X)>0.5) & DEAs$chronic.org$FDR<=0.05 & DEAs$chronic.org$logCPM > 0)]

MixN_org_chronic <- SEs$chronic.org[,which(SEs$chronic.org$EXPO=="CNT"|SEs$chronic.org$EXPO=="1X" | SEs$chronic.org$EXPO=="1000X")]

sehm(MixN_org_chronic, assayName = "corrected", org.chronic,  do.scale = T, show_rownames = F, anno_columns = c("Line","EXPO2"), main="Chronic org DEGs")

1X vs 1000X

FDRTh <- 0.05
LogFCTh <- 0.5

MixN1X <- DEAs$chronic.org
MixN1X$genes <- row.names(MixN1X)
MixN1X$logFC <- (MixN1X$logFC.EXPO1X)

MixN1000X <- DEAs$chronic.org
MixN1000X$genes <- row.names(MixN1000X)
MixN1000X$logFC <- (MixN1X$logFC.EXPO1000X)

#Comp <- compareResultsFC(MixN1X, MixN1000X, FDRth=0.05, FCth=2^0.5, FDRceil=1e-10, logCPMth = 0, FCceil=8, title='Comparing MixN 1X to 1000X', geneLabel=TRUE)

#Comp$Scatter

Comp <- compareResultsFCNew(MixN1X, MixN1000X, FDRth=0.05, FCth=2^0.5, FDRceil=1e-10, logCPMth=0, FCceil=2.5, 
                              title='Comparing MixN 1X to 1000X', geneLabel=TRUE, topLab=-16)
## Warning in cor.test.default(AllRes$logFC_A, AllRes$logFC_B, method = corMethod):
## Cannot compute exact p-value with ties

Comp$Scatter

ggsave(Comp$Scatter, filename='Data/ChronicOrganoids/Scatter.pdf', width=10, height=10)
ggsave(Comp$Scatter, filename='Data/ChronicOrganoids/Scatter.png', width=10, height=10)

Neurodevelopmental Disorder (NDD) Genes

load("Data/ASD.RData", verbose = T)
## Loading objects:
##   ASD
##   SFARI
##   SFARIgenes
##   AutismKB
##   DecipherNeuro
##   MSSNG
##   ASDsevere
##   ID
##   NeuroOmim
##   iPSYCH
##   GDI
##   NeuropsychiatricDiseases
org.chronic3 <- row.names(DEAs$chronic.org)[which(DEAs$chronic.org$FDR<=0.05)]
controlNeg <- row.names(DEAs$chronic.org)[-which(DEAs$chronic.org$FDR<=0.05)]

random1 <- sample(row.names(DEAs$chronic.org), 10)
random2 <- sample(row.names(DEAs$chronic.org), 100)
random3 <- sample(row.names(DEAs$chronic.org), 1000)

chronic.orgUp <- row.names(DEAs$chronic.org)[which(((DEAs$chronic.org$logFC.EXPO1X)>0 & (DEAs$chronic.org$logFC.EXPO1000X)>0) & DEAs$chronic.org$FDR<=0.05)]
chronic.orgDown <- row.names(DEAs$chronic.org)[which(((DEAs$chronic.org$logFC.EXPO1X)<0 & (DEAs$chronic.org$logFC.EXPO1000X)<0) & DEAs$chronic.org$FDR<=0.05)]


MixN <- list(organoidsDEGs=org.chronic3, organoidsDEGsUp=chronic.orgUp, organoidsDEGsDown=chronic.orgDown,NonAffectedGenes=controlNeg, random1=random1,random2=random2,random3=random3 )

m <- overlapper::multintersect(ll = MixN, ll2 = ASD, universe = row.names(DEAs$chronic.fetal))
#overlapper::dotplot.multintersect(m,sizeRange = c(0,15))

org.chronicLong<-org.chronic3
save(org.chronic,org.chronicLong,file = "Data/DEGsOrganoidsChronic.RData")

dotplot.multintersect.mod(m, sizeRange = c(0,15), th=0.05)

ggsave(dotplot.multintersect.mod(m, th=0.05), filename='Data/ChronicOrganoids/DotplotFetalNDD.pdf', width=10, height=6)

m$prob <- round(m$prob, digits=3)
#DT::datatable(m$prob)

SFARI genes

m <- overlapper::multintersect(ll = MixN, ll2 = SFARIgenes, universe = row.names(DEAs$chronic.fetal))

dotplot.multintersect.mod(m, th=0.05)

ggsave(dotplot.multintersect.mod(m, th=0.05), filename='Data/ChronicOrganoids/DotplotFetalSFARI.pdf', width=10, height=6)

NPD genes

m <- overlapper::multintersect(ll = MixN, ll2 = NeuropsychiatricDiseases, universe = row.names(DEAs$chronic.fetal))

dotplot.multintersect.mod(m, th=0.05)

ggsave(dotplot.multintersect.mod(m, th=0.05), filename='Data/ChronicOrganoids/DotplotFetalNPD.pdf', width=10, height=6)

testing a different gene overlapper

https://bioconductor.org/packages/release/bioc/html/GeneOverlap.html

library(GeneOverlap)

gom.obj <- newGOM(MixN,ASD, genome.size=length(row.names(DEAs$chronic.org)))
print(gom.obj)
## A GeneOverlapMatrix object:
## ###### Intersection ######
##                   SFARI AutismKB Decipher MSSNG iPSYCH NeuroOMIM ASDsevere  ID
## organoidsDEGs       172       39       21    15     29        15        40  43
## organoidsDEGsUp      53        7        9     4     10         9        14  12
## organoidsDEGsDown    68       20        7    10     13         2        19  21
## NonAffectedGenes    673      178      134    39     73        65       167 144
## random1               0        0        0     0      0         0         0   0
## random2               4        0        2     0      0         1         0   0
## random3              56       11       12     3      8         4        13  18
##                   SingleCellASD NeuronASD AstrocyteASD NeuropsychiatricDiseases
## organoidsDEGs               109        26            8                      226
## organoidsDEGsUp              24         5            0                       76
## organoidsDEGsDown            57        17            7                       87
## NonAffectedGenes            344        69           41                      935
## random1                       0         0            0                        1
## random2                       2         0            1                        6
## random3                      33         7            3                       65
## ###### P-value ######
##                        SFARI  AutismKB  Decipher       MSSNG       iPSYCH
## organoidsDEGs     0.02231899 0.2882567 0.8344645 0.018552716 0.0006840381
## organoidsDEGsUp   0.14824495 0.9237283 0.3571956 0.268247645 0.0239544632
## organoidsDEGsDown 0.47977370 0.1786488 0.9380662 0.004489613 0.0260206988
## NonAffectedGenes  1.00000000 0.9958340 0.6417477 0.996755940 0.9997043028
## random1           1.00000000 1.0000000 1.0000000 1.000000000 1.0000000000
## random2           0.84602444 1.0000000 0.2570886 1.000000000 1.0000000000
## random3           0.66744915 0.8328923 0.2765499 0.663609205 0.2875722940
##                   NeuroOMIM ASDsevere         ID SingleCellASD   NeuronASD
## organoidsDEGs     1.0000000 0.4769800 0.00783293  0.0003390561 0.015528828
## organoidsDEGsUp   0.9554060 0.3202309 0.20909608  0.5742959087 0.601455098
## organoidsDEGsDown 1.0000000 0.4070359 0.02858962  0.0003726425 0.001505528
## NonAffectedGenes  1.0000000 1.0000000 0.99976073  1.0000000000 0.999999983
## random1           1.0000000 1.0000000 1.00000000  1.0000000000 1.000000000
## random2           0.8465324 1.0000000 1.00000000  0.8269194222 1.000000000
## random3           0.9999934 0.7779795 0.04787005  0.4122299829 0.505493712
##                   AstrocyteASD NeuropsychiatricDiseases
## organoidsDEGs        0.7377828             0.0007054163
## organoidsDEGsUp      1.0000000             0.0061456247
## organoidsDEGsDown    0.1258210             0.3705189362
## NonAffectedGenes     0.9994944             0.9999999973
## random1              1.0000000             0.5324600843
## random2              0.3089789             0.7491843066
## random3              0.7203668             0.8626548875
## ###### Odds Ratio ######
##                       SFARI  AutismKB  Decipher     MSSNG    iPSYCH  NeuroOMIM
## organoidsDEGs     1.1983919 1.1192904 0.8166280 2.0367785 2.1644293 0.27878640
## organoidsDEGsUp   1.1784266 0.6274643 1.1891777 1.5648136 2.1796504 0.60720421
## organoidsDEGsDown 1.0129806 1.2786952 0.6036833 2.9608197 1.9457765 0.08673027
## NonAffectedGenes  0.4071181 0.6529367 0.9512470 0.4485827 0.4620156 0.04618727
## random1           0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.00000000
## random2           0.6664929 0.0000000 2.0737493 0.0000000 0.0000000 0.53402587
## random3           0.9478374 0.7737971 1.2445912 0.8831531 1.3057397 0.20237707
##                   ASDsevere        ID SingleCellASD NeuronASD AstrocyteASD
## organoidsDEGs     1.0220948 1.5724179     1.4816506 1.7034684    0.8325990
## organoidsDEGsUp   1.1746139 1.3327842     0.9767273 0.9571983    0.0000000
## organoidsDEGsDown 1.0816286 1.6385936     1.6902996 2.4694903    1.7542714
## NonAffectedGenes  0.3550056 0.5493021     0.3599084 0.3151439    0.3967105
## random1           0.0000000 0.0000000     0.0000000 0.0000000    0.0000000
## random2           0.0000000 0.0000000     0.6271536 0.0000000    2.7654528
## random3           0.8307804 1.5948446     1.0546567 1.0513365    0.8054194
##                   NeuropsychiatricDiseases
## organoidsDEGs                    1.2900641
## organoidsDEGsUp                  1.3919764
## organoidsDEGsDown                1.0445669
## NonAffectedGenes                 0.6423804
## random1                          1.4073308
## random2                          0.8073267
## random3                          0.8734341
## ###### Jaccard Index ######
##                         SFARI    AutismKB    Decipher       MSSNG      iPSYCH
## organoidsDEGs     0.051667167 0.014275256 0.007829978 0.005807201 0.011085627
## organoidsDEGsUp   0.031454006 0.006993007 0.009667025 0.004813478 0.011467890
## organoidsDEGsDown 0.033415233 0.014781966 0.005392912 0.008403361 0.010534846
## NonAffectedGenes  0.047879909 0.012878952 0.009712256 0.002828752 0.005289855
## random1           0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
## random2           0.003795066 0.000000000 0.007751938 0.000000000 0.000000000
## random3           0.029442692 0.009038620 0.010452962 0.002851711 0.007312614
##                     NeuroOMIM  ASDsevere         ID SingleCellASD   NeuronASD
## organoidsDEGs     0.005300353 0.01451906 0.01597325   0.036986766 0.009900990
## organoidsDEGsUp   0.008387698 0.01375246 0.01250000   0.018912530 0.005656109
## organoidsDEGsDown 0.001384083 0.01378810 0.01595745   0.035602748 0.013742926
## NonAffectedGenes  0.004640206 0.01205254 0.01042044   0.024677188 0.004996018
## random1           0.000000000 0.00000000 0.00000000   0.000000000 0.000000000
## random2           0.002493766 0.00000000 0.00000000   0.003273322 0.000000000
## random3           0.003081664 0.01049233 0.01533220   0.022297297 0.006352087
##                   AstrocyteASD NeuropsychiatricDiseases
## organoidsDEGs      0.003082852             0.0643691256
## organoidsDEGsUp    0.000000000             0.0400421496
## organoidsDEGsDown  0.005843072             0.0386323268
## NonAffectedGenes   0.002973169             0.0666429081
## random1            0.000000000             0.0008312552
## random2            0.006289308             0.0046583851
## random3            0.002838221             0.0305307656
drawHeatmap(gom.obj)

SFARI genes

gom.obj <- newGOM(MixN,SFARIgenes, genome.size=length(row.names(DEAs$chronic.org)))
drawHeatmap(gom.obj)

NPD genes

gom.obj <- newGOM(MixN,NeuropsychiatricDiseases, genome.size=length(row.names(DEAs$chronic.org)))
drawHeatmap(gom.obj)

Gene expression dysregulation of the most important NDD genes

geneStripPairEDCMix(SE = MixN_org_chronic,GeneSet =intersect(org.chronic, union(union(SFARIgenes$score1,SFARIgenes$score2),SFARIgenes$score3)), printExp = FALSE, SampleColors = "Default")
## Warning: `fun.y` is deprecated. Use `fun` instead.
## Warning: `fun.ymin` is deprecated. Use `fun.min` instead.
## Warning: `fun.ymax` is deprecated. Use `fun.max` instead.

PVals <- DEAs$chronic.org[intersect(org.chronic, unlist(SFARIgenes)),]
PVals <- PVals[complete.cases(PVals), ]
PVals
##          logFC.EXPO1X logFC.EXPO1000X    logCPM         F       PValue
## SCN9A      0.16332099     -0.73394503 3.6238927 28.679243 4.486322e-08
## NEFL       0.17224825     -0.67496037 7.2905518 26.587394 1.004741e-07
## CACNA1E   -0.22395439     -0.68438361 4.9734221 21.775175 7.506001e-07
## SCN2A     -0.23500980     -0.75882170 4.4730437 21.537869 8.340935e-07
## PCDH9     -0.35329648     -0.51191575 6.2629341 18.479058 3.451823e-06
## CDH9      -0.63189859     -1.50276593 0.4498228 17.344314 6.030875e-06
## SPARCL1    0.28517770     -0.59496470 5.7544922 15.403606 1.636156e-05
## TH         0.31734183     -0.56675217 2.3380022 14.953642 2.079647e-05
## NEGR1     -0.04352262     -0.54219055 4.4452160 14.906921 2.132497e-05
## SLC6A1    -0.21067534     -0.57822638 3.0876260 13.077681 5.868259e-05
## NRXN3     -0.50939333      0.21190259 5.1255620 12.755676 7.057116e-05
## KCNMA1    -0.65819131     -0.33457106 2.9964461 12.306480 9.158739e-05
## GRM8       0.59891403      0.30232488 5.4549395 11.619416 1.375170e-04
## CACNB2    -0.25652132     -0.51771213 3.2092017 11.613580 1.379983e-04
## DPYD      -0.41965092     -0.59127083 3.0251296 11.342770 1.624122e-04
## LMX1B      0.22470186     -0.62563738 4.3620879 11.132867 1.844648e-04
## KDM6B      0.28440194      0.54084969 5.1366501 10.480879 2.756171e-04
## WNT1       0.42357343     -0.73059040 1.7103686 10.118513 3.459453e-04
## FABP3     -0.54794627     -0.50839479 3.3966961  9.978136 3.780888e-04
## CNTN5      0.05897304     -0.60625916 1.6670097  8.563565 9.503601e-04
## DRD2       0.17067356     -0.65784667 1.0232263  8.552201 9.576166e-04
## KIRREL3   -0.06077077     -0.51471524 2.7742917  8.078676 1.318723e-03
## PCDH11X   -0.32805473     -0.64062334 4.6909851  7.525175 1.931552e-03
## GRM7       0.05663132     -0.61625257 2.1733713  7.382859 2.133643e-03
## SLC4A10    0.10963873      0.57241339 5.2768430  7.242564 2.354874e-03
## ACE        0.50518870     -0.00501186 1.4608377  7.221730 2.389741e-03
## FABP7     -0.58477173     -0.21714537 4.9423324  6.660712 3.567096e-03
## THBS1     -0.55858202     -0.12754071 3.7180289  6.643538 3.611637e-03
## FEZF2      0.33938486      0.89684384 2.2713893  6.558978 3.839691e-03
## ADAMTS18   0.30426384      0.60810219 3.1172417  6.255539 4.791842e-03
## SAMD11     0.61769439      0.16814633 2.5613572  5.718011 7.145064e-03
## CACNA1I   -0.56456711     -0.82961573 1.3985198  5.689791 7.298381e-03
## FOXP2     -0.57048773      0.03921951 5.9517196  5.616600 7.712461e-03
##                   FDR
## SCN9A    0.0001156523
## NEFL     0.0001170810
## CACNA1E  0.0003182873
## SCN2A    0.0003239857
## PCDH9    0.0006120982
## CDH9     0.0008920245
## SPARCL1  0.0014428240
## TH       0.0016448800
## NEGR1    0.0016487940
## SLC6A1   0.0029098719
## NRXN3    0.0031645244
## KCNMA1   0.0036541645
## GRM8     0.0047692248
## CACNB2   0.0047697124
## DPYD     0.0052581297
## LMX1B    0.0057095415
## KDM6B    0.0070921402
## WNT1     0.0081205050
## FABP3    0.0084843741
## CNTN5    0.0146529435
## DRD2     0.0147116784
## KIRREL3  0.0175908790
## PCDH11X  0.0222464372
## GRM7     0.0234873475
## SLC4A10  0.0246898511
## ACE      0.0249591803
## FABP7    0.0313881338
## THBS1    0.0316627215
## FEZF2    0.0327906209
## ADAMTS18 0.0371020919
## SAMD11   0.0473454810
## CACNA1I  0.0477600401
## FOXP2    0.0495748998

• Genes with conventional PVal > 0.05:
• Genes with conventional PVal < 0.05:
• Genes with conventional PVal < 0.01: SCN9A, NEFL, CACNA1E, SCN2A, PCDH9, CDH9, SPARCL1, TH, NEGR1, SLC6A1, NRXN3, KCNMA1, GRM8, CACNB2, DPYD, LMX1B, KDM6B, WNT1, FABP3, CNTN5, DRD2, KIRREL3, PCDH11X, GRM7, SLC4A10, ACE, FABP7, THBS1, FEZF2, ADAMTS18, SAMD11, CACNA1I, FOXP2

Gene expression dysregulation of the fetal NDD-DEGs in organoids dataset

load("Data/DEGsFetalChronic.RData", verbose = T)
## Loading objects:
##   fetal.chronic
##   fetal.chronicLong
geneStripPairEDCMix(SE = MixN_org_chronic,GeneSet = intersect(fetal.chronic, unlist(SFARIgenes)), printExp = FALSE,SampleColors = "Default")
## [1] "Expression values are not available for the following genes: CD38"
## Warning: `fun.y` is deprecated. Use `fun` instead.
## Warning: `fun.ymin` is deprecated. Use `fun.min` instead.
## Warning: `fun.ymax` is deprecated. Use `fun.max` instead.

PVals <- DEAs$chronic.org[intersect(fetal.chronic, unlist(SFARIgenes)),]
PVals <- PVals[complete.cases(PVals), ]
PVals
##         logFC.EXPO1X logFC.EXPO1000X   logCPM        F      PValue        FDR
## RANBP17   -0.1626618      -0.2152368 5.277389 4.470447 0.018742025 0.08479478
## UPF3B      0.0328277      -0.2586830 3.815079 2.967897 0.064606056 0.17889598
## KCNJ2      0.4778245       0.1451159 6.134523 5.280253 0.009961453 0.05816433

• Genes with conventional PVal > 0.05: UPF3B
• Genes with conventional PVal < 0.05: RANBP17
• Genes with conventional PVal < 0.01: KCNJ2

Gene expression dysregulation of the genes found in the acute expo

load("Data/DEGsFetalAcute.RData", verbose = T)
## Loading objects:
##   fet.acute
geneStripPairEDCMix(SE = MixN_org_chronic,GeneSet = intersect(fet.acute,org.chronic), printExp = FALSE,SampleColors = "Default")
## Warning: `fun.y` is deprecated. Use `fun` instead.
## Warning: `fun.ymin` is deprecated. Use `fun.min` instead.
## Warning: `fun.ymax` is deprecated. Use `fun.max` instead.

PVals <- DEAs$chronic.org[intersect(fet.acute,org.chronic),]
PVals <- PVals[complete.cases(PVals), ]
PVals
##      logFC.EXPO1X logFC.EXPO1000X   logCPM        F       PValue         FDR
## NEFL    0.1722482      -0.6749604 7.290552 26.58739 1.004741e-07 0.000117081

• Genes with conventional PVal > 0.05:
• Genes with conventional PVal < 0.05:
• Genes with conventional PVal < 0.01: NEFL

geneStripPairEDCMix(SE = MixN_org_chronic,GeneSet = c("CLSTN2", "EPHB2"), printExp = FALSE,SampleColors = "Default")
## Warning: `fun.y` is deprecated. Use `fun` instead.
## Warning: `fun.ymin` is deprecated. Use `fun.min` instead.
## Warning: `fun.ymax` is deprecated. Use `fun.max` instead.

PVals <- DEAs$chronic.org[c("CLSTN2", "EPHB2"),]
PVals <- PVals[complete.cases(PVals), ]
PVals
##        logFC.EXPO1X logFC.EXPO1000X   logCPM        F     PValue       FDR
## CLSTN2  0.214876852      0.09619874 5.239390 1.447981 0.24891393 0.4130782
## EPHB2   0.006988673      0.16640719 7.820478 3.802717 0.03213182 0.1164109

• Genes with conventional PVal > 0.05: CLSTN2
• Genes with conventional PVal < 0.05: EPHB2
• Genes with conventional PVal < 0.01:

Comparison across systems

load("Data/DEGsFetalChronic.RData", verbose = T)
## Loading objects:
##   fetal.chronic
##   fetal.chronicLong
load("Data/DEGsOrganoidsChronic.RData", verbose = T)
## Loading objects:
##   org.chronic
##   org.chronicLong

MixN <- list(OrganoidsDEGs=org.chronic, FetalAcuteDEGs=fet.acute, FetalChronicDEGs=fetal.chronic)

m<-multintersect(ll = MixN, universe = row.names(filterGenes(Total)))
dotplot.multintersect.mod(m, th=0.05)
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_text).

m<-multintersect(ll = MixN, ll2 = ASD,  universe = row.names(filterGenes(Total)))
dotplot.multintersect.mod(m, th=0.05)

m<-multintersect(ll = MixN, ll2 = SFARIgenes,  universe = row.names(filterGenes(Total)))
dotplot.multintersect.mod(m, th=0.05)

m<-multintersect(ll = MixN, ll2 = NeuropsychiatricDiseases,  universe = row.names(filterGenes(Total)))
dotplot.multintersect.mod(m, th=0.05)

load("Data/DEGsFetalChronic.RData", verbose = T)
## Loading objects:
##   fetal.chronic
##   fetal.chronicLong
load("Data/DEGsOrganoidsChronic.RData", verbose = T)
## Loading objects:
##   org.chronic
##   org.chronicLong

MixN <- list(OrganoidsDEGs=org.chronicLong, FetalAcuteDEGs=fet.acute, FetalChronicDEGs=fetal.chronicLong)

m<-multintersect(ll = MixN, universe = row.names(filterGenes(Total)))
dotplot.multintersect.mod(m, th=0.05)
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_text).

m<-multintersect(ll = MixN, ll2 = ASD,  universe = row.names(filterGenes(Total)))
dotplot.multintersect.mod(m, th=0.05)

m<-multintersect(ll = MixN, ll2 = SFARIgenes,  universe = row.names(filterGenes(Total)))
dotplot.multintersect.mod(m, th=0.05)

m<-multintersect(ll = MixN, ll2 = NeuropsychiatricDiseases,  universe = row.names(filterGenes(Total)))
dotplot.multintersect.mod(m, th=0.05)

Data praparation

For details on data filtering, normalization, batch correction and differential expression analysis, see here

For details on the preparation of Neurodevelopmental disorder genes, see here

---

Index

MixN_Acute

MixN_ChronicFetal

FunctionalFetalChronicMixN

MixN_ChronicOrganoids

FunctionalOrganoidChronicMixN

MixNVsSingleCompounds

MasterRegulatorAnalysis

HormonalPathways

Immunofluorescence

MixN_clustering

FunctionalClustering

Authors

Nicolò Caporale: nicolo.caporale@ieo.it, nicolo.caporale@unimi.it

Cristina Cheroni: cristina.cheroni@ieo.it

Pierre-Luc Germain: pierre-luc.germain@ieo.it

Giuseppe Testa: giuseppe.testa@ieo.it

Lab: http://www.testalab.eu/

‘Date: February 24, 2021’

sessionInfo()

## R version 4.0.3 (2020-10-10)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Catalina 10.15.7
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] parallel  stats4    stats     graphics  grDevices utils     datasets 
## [8] methods   base     
## 
## other attached packages:
##  [1] GeneOverlap_1.26.0          ggrepel_0.9.1              
##  [3] overlapper_0.99.1           reshape2_1.4.4             
##  [5] sva_3.38.0                  BiocParallel_1.24.1        
##  [7] genefilter_1.72.1           mgcv_1.8-34                
##  [9] nlme_3.1-152                dplyr_1.0.4                
## [11] plotly_4.9.3                ggplot2_3.3.3              
## [13] pheatmap_1.0.12             DT_0.17                    
## [15] edgeR_3.32.1                limma_3.46.0               
## [17] SEtools_1.4.0               SummarizedExperiment_1.20.0
## [19] Biobase_2.50.0              GenomicRanges_1.42.0       
## [21] GenomeInfoDb_1.26.2         IRanges_2.24.1             
## [23] S4Vectors_0.28.1            BiocGenerics_0.36.0        
## [25] MatrixGenerics_1.2.1        matrixStats_0.58.0         
## 
## loaded via a namespace (and not attached):
##   [1] circlize_0.4.12        plyr_1.8.6             lazyeval_0.2.2        
##   [4] shinydashboard_0.7.1   splines_4.0.3          digest_0.6.27         
##   [7] foreach_1.5.1          htmltools_0.5.1.1      fansi_0.4.2           
##  [10] magrittr_2.0.1         memoise_2.0.0          cluster_2.1.1         
##  [13] openxlsx_4.2.3         ComplexHeatmap_2.6.2   annotate_1.68.0       
##  [16] colorspace_2.0-0       blob_1.2.1             xfun_0.21             
##  [19] crayon_1.4.1           RCurl_1.98-1.2         jsonlite_1.7.2        
##  [22] survival_3.2-7         iterators_1.0.13       glue_1.4.2            
##  [25] registry_0.5-1         gtable_0.3.0           zlibbioc_1.36.0       
##  [28] XVector_0.30.0         UpSetR_1.4.0           GetoptLong_1.0.5      
##  [31] DelayedArray_0.16.1    V8_3.4.0               shape_1.4.5           
##  [34] scales_1.1.1           futile.options_1.0.1   DBI_1.1.1             
##  [37] randomcoloR_1.1.0.1    Rcpp_1.0.6             viridisLite_0.3.0     
##  [40] xtable_1.8-4           clue_0.3-58            bit_4.0.4             
##  [43] htmlwidgets_1.5.3      httr_1.4.2             gplots_3.1.1          
##  [46] RColorBrewer_1.1-2     ellipsis_0.3.1         farver_2.0.3          
##  [49] pkgconfig_2.0.3        XML_3.99-0.5           sass_0.3.1            
##  [52] locfit_1.5-9.4         utf8_1.1.4             labeling_0.4.2        
##  [55] tidyselect_1.1.0       rlang_0.4.10           later_1.1.0.1         
##  [58] AnnotationDbi_1.52.0   munsell_0.5.0          tools_4.0.3           
##  [61] cachem_1.0.4           generics_0.1.0         RSQLite_2.2.3         
##  [64] evaluate_0.14          stringr_1.4.0          fastmap_1.1.0         
##  [67] yaml_2.2.1             knitr_1.31             bit64_4.0.5           
##  [70] shinycssloaders_1.0.0  zip_2.1.1              caTools_1.18.1        
##  [73] purrr_0.3.4            mime_0.10              formatR_1.7           
##  [76] compiler_4.0.3         curl_4.3               png_0.1-7             
##  [79] tibble_3.0.6           bslib_0.2.4            stringi_1.5.3         
##  [82] highr_0.8              futile.logger_1.4.3    lattice_0.20-41       
##  [85] Matrix_1.3-2           ggsci_2.9              vctrs_0.3.6           
##  [88] pillar_1.5.0           lifecycle_1.0.0        jquerylib_0.1.3       
##  [91] GlobalOptions_0.1.2    cowplot_1.1.1          data.table_1.14.0     
##  [94] bitops_1.0-6           seriation_1.2-9        httpuv_1.5.5          
##  [97] R6_2.5.0               promises_1.2.0.1       TSP_1.1-10            
## [100] KernSmooth_2.23-18     gridExtra_2.3          codetools_0.2-18      
## [103] lambda.r_1.2.4         gtools_3.8.2           assertthat_0.2.1      
## [106] rjson_0.2.20           withr_2.4.1            GenomeInfoDbData_1.2.4
## [109] VennDiagram_1.6.20     grid_4.0.3             tidyr_1.1.2           
## [112] rmarkdown_2.7          Cairo_1.5-12.2         Rtsne_0.15            
## [115] shiny_1.6.0